Overview

Dataset statistics

Number of variables21
Number of observations201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.5 KiB
Average record size in memory176.0 B

Variable types

Categorical3
Text1
Unsupported4
Numeric13

Alerts

class has constant value "Neuropathic"Constant
flag has constant value "real"Constant
file_path has unique valuesUnique
rms_acc_x has unique valuesUnique
rms_acc_y has unique valuesUnique
path has unique valuesUnique
samp_en_x has unique valuesUnique
samp_en_y has unique valuesUnique
f80_x has unique valuesUnique
f80_y has unique valuesUnique
mf_lf_x has unique valuesUnique
mf_lf_y has unique valuesUnique
mf_mf_x has unique valuesUnique
mf_mf_y has unique valuesUnique
mf_hf_x has unique valuesUnique
mf_hf_y has unique valuesUnique
cop_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
cop_y is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_y is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-22 08:30:36.884300
Analysis finished2024-05-22 08:30:51.615274
Duration14.73 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

class
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Neuropathic
201 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2211
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeuropathic
2nd rowNeuropathic
3rd rowNeuropathic
4th rowNeuropathic
5th rowNeuropathic

Common Values

ValueCountFrequency (%)
Neuropathic 201
100.0%

Length

2024-05-22T04:30:51.684291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:30:51.777018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
neuropathic 201
100.0%

Most occurring characters

ValueCountFrequency (%)
N 201
9.1%
e 201
9.1%
u 201
9.1%
r 201
9.1%
o 201
9.1%
p 201
9.1%
a 201
9.1%
t 201
9.1%
h 201
9.1%
i 201
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 201
9.1%
e 201
9.1%
u 201
9.1%
r 201
9.1%
o 201
9.1%
p 201
9.1%
a 201
9.1%
t 201
9.1%
h 201
9.1%
i 201
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 201
9.1%
e 201
9.1%
u 201
9.1%
r 201
9.1%
o 201
9.1%
p 201
9.1%
a 201
9.1%
t 201
9.1%
h 201
9.1%
i 201
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 201
9.1%
e 201
9.1%
u 201
9.1%
r 201
9.1%
o 201
9.1%
p 201
9.1%
a 201
9.1%
t 201
9.1%
h 201
9.1%
i 201
9.1%

file_path
Text

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:51.939067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length62
Median length61
Mean length59.885572
Min length56

Characters and Unicode

Total characters12037
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)100.0%

Sample

1st row../data/raw/COP_Tests/DNG\participant_12\EBOA 1_SWARII.txt
2nd row../data/raw/COP_Tests/DNG\participant_12\EBOA 2_SWARII.txt
3rd row../data/raw/COP_Tests/DNG\participant_12\EBOA 3_SWARII.txt
4th row../data/raw/COP_Tests/DNG\participant_12\EBOASI 1_SWARII.txt
5th row../data/raw/COP_Tests/DNG\participant_12\EBOASI 2_SWARII.txt
ValueCountFrequency (%)
2_swarii.txt 65
 
16.8%
1_swarii.txt 62
 
16.1%
3_swarii.txt 58
 
15.0%
data/raw/cop_tests/dng\participant_12\eboa 3
 
0.8%
data/raw/cop_tests/dng\participant_12\eboasi 3
 
0.8%
data/raw/cop_tests/dng\participant_12\eboctdsi 3
 
0.8%
data/raw/cop_tests/dng\participant_14\eboa 3
 
0.8%
data/raw/cop_tests/dng\participant_13\eboctd 3
 
0.8%
data/raw/cop_tests/dng\participant_13\ebocsitd 3
 
0.8%
data/raw/cop_tests/dng\participant_13\ebocsi 3
 
0.8%
Other values (74) 180
46.6%
2024-05-22T04:30:52.330143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1206
 
10.0%
a 1005
 
8.3%
/ 804
 
6.7%
. 603
 
5.0%
_ 603
 
5.0%
I 500
 
4.2%
r 402
 
3.3%
O 402
 
3.3%
i 402
 
3.3%
p 402
 
3.3%
Other values (31) 5708
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1206
 
10.0%
a 1005
 
8.3%
/ 804
 
6.7%
. 603
 
5.0%
_ 603
 
5.0%
I 500
 
4.2%
r 402
 
3.3%
O 402
 
3.3%
i 402
 
3.3%
p 402
 
3.3%
Other values (31) 5708
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1206
 
10.0%
a 1005
 
8.3%
/ 804
 
6.7%
. 603
 
5.0%
_ 603
 
5.0%
I 500
 
4.2%
r 402
 
3.3%
O 402
 
3.3%
i 402
 
3.3%
p 402
 
3.3%
Other values (31) 5708
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1206
 
10.0%
a 1005
 
8.3%
/ 804
 
6.7%
. 603
 
5.0%
_ 603
 
5.0%
I 500
 
4.2%
r 402
 
3.3%
O 402
 
3.3%
i 402
 
3.3%
p 402
 
3.3%
Other values (31) 5708
47.4%

exam
Categorical

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
EBOATD
26 
EBOC
26 
EBOCTD
26 
EBOA
25 
EBOASI
25 
Other values (5)
73 

Length

Max length8
Median length6
Mean length5.9701493
Min length4

Characters and Unicode

Total characters1200
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEBOA
2nd rowEBOA
3rd rowEBOA
4th rowEBOASI
5th rowEBOASI

Common Values

ValueCountFrequency (%)
EBOATD 26
12.9%
EBOC 26
12.9%
EBOCTD 26
12.9%
EBOA 25
12.4%
EBOASI 25
12.4%
EBOCSI 25
12.4%
EBOASITD 16
8.0%
EBOCSITD 15
7.5%
EBOCTDSI 9
 
4.5%
EBOATDSI 8
 
4.0%

Length

2024-05-22T04:30:52.512183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:30:52.706226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
eboatd 26
12.9%
eboc 26
12.9%
eboctd 26
12.9%
eboa 25
12.4%
eboasi 25
12.4%
ebocsi 25
12.4%
eboasitd 16
8.0%
ebocsitd 15
7.5%
eboctdsi 9
 
4.5%
eboatdsi 8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
E 201
16.8%
B 201
16.8%
O 201
16.8%
C 101
8.4%
A 100
8.3%
T 100
8.3%
D 100
8.3%
S 98
8.2%
I 98
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 201
16.8%
B 201
16.8%
O 201
16.8%
C 101
8.4%
A 100
8.3%
T 100
8.3%
D 100
8.3%
S 98
8.2%
I 98
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 201
16.8%
B 201
16.8%
O 201
16.8%
C 101
8.4%
A 100
8.3%
T 100
8.3%
D 100
8.3%
S 98
8.2%
I 98
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 201
16.8%
B 201
16.8%
O 201
16.8%
C 101
8.4%
A 100
8.3%
T 100
8.3%
D 100
8.3%
S 98
8.2%
I 98
8.2%

cop_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.1 KiB

cop_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.1 KiB

flag
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
real
201 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters804
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreal
2nd rowreal
3rd rowreal
4th rowreal
5th rowreal

Common Values

ValueCountFrequency (%)
real 201
100.0%

Length

2024-05-22T04:30:52.910270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-22T04:30:53.010292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
real 201
100.0%

Most occurring characters

ValueCountFrequency (%)
r 201
25.0%
e 201
25.0%
a 201
25.0%
l 201
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 201
25.0%
e 201
25.0%
a 201
25.0%
l 201
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 201
25.0%
e 201
25.0%
a 201
25.0%
l 201
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 201
25.0%
e 201
25.0%
a 201
25.0%
l 201
25.0%

acc_x
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.1 KiB

acc_y
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.1 KiB

rms_acc_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4140525
Minimum0.21437957
Maximum4.6094109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:53.144324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.21437957
5-th percentile0.3606697
Q10.69056731
median1.1863004
Q31.9861165
95-th percentile3.3609017
Maximum4.6094109
Range4.3950313
Interquartile range (IQR)1.2955492

Descriptive statistics

Standard deviation0.93790043
Coefficient of variation (CV)0.6632713
Kurtosis0.49970572
Mean1.4140525
Median Absolute Deviation (MAD)0.57651294
Skewness1.0120725
Sum284.22455
Variance0.87965722
MonotonicityNot monotonic
2024-05-22T04:30:53.300377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2574796197 1
 
0.5%
3.548915014 1
 
0.5%
3.219192351 1
 
0.5%
2.743535902 1
 
0.5%
4.000017208 1
 
0.5%
2.427105829 1
 
0.5%
2.244953226 1
 
0.5%
2.754464173 1
 
0.5%
1.21223 1
 
0.5%
2.544718265 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.2143795731 1
0.5%
0.2483613759 1
0.5%
0.2574796197 1
0.5%
0.270346477 1
0.5%
0.2729954098 1
0.5%
0.2931276359 1
0.5%
0.2934060178 1
0.5%
0.2991096173 1
0.5%
0.3346370787 1
0.5%
0.3364756527 1
0.5%
ValueCountFrequency (%)
4.609410891 1
0.5%
4.395143256 1
0.5%
4.000017208 1
0.5%
3.877103739 1
0.5%
3.548915014 1
0.5%
3.451415239 1
0.5%
3.404415649 1
0.5%
3.392539339 1
0.5%
3.38575885 1
0.5%
3.380526554 1
0.5%

rms_acc_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3761097
Minimum0.6934246
Maximum9.5625607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:53.515423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.6934246
5-th percentile0.86851528
Q11.2762987
median1.9219305
Q32.7609311
95-th percentile6.3102364
Maximum9.5625607
Range8.8691361
Interquartile range (IQR)1.4846324

Descriptive statistics

Standard deviation1.6719201
Coefficient of variation (CV)0.70363757
Kurtosis4.4691884
Mean2.3761097
Median Absolute Deviation (MAD)0.7223374
Skewness2.0755001
Sum477.59806
Variance2.7953168
MonotonicityNot monotonic
2024-05-22T04:30:53.672457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8754507799 1
 
0.5%
3.196508795 1
 
0.5%
5.84866833 1
 
0.5%
5.188394764 1
 
0.5%
7.666915633 1
 
0.5%
6.837457697 1
 
0.5%
3.937907679 1
 
0.5%
2.961786026 1
 
0.5%
2.927118585 1
 
0.5%
2.426128533 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.693424598 1
0.5%
0.7000164369 1
0.5%
0.7397948762 1
0.5%
0.74331324 1
0.5%
0.761720027 1
0.5%
0.7710135176 1
0.5%
0.7754037944 1
0.5%
0.7963647093 1
0.5%
0.8037851197 1
0.5%
0.8589319505 1
0.5%
ValueCountFrequency (%)
9.562560662 1
0.5%
8.622623546 1
0.5%
8.517517384 1
0.5%
8.019267185 1
0.5%
8.017226084 1
0.5%
7.666915633 1
0.5%
6.952344885 1
0.5%
6.862460434 1
0.5%
6.837457697 1
0.5%
6.765803089 1
0.5%

path
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.05067
Minimum125.86419
Maximum1100.9568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:53.829496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum125.86419
5-th percentile171.89509
Q1283.61758
median402.21982
Q3502.45751
95-th percentile742.90931
Maximum1100.9568
Range975.0926
Interquartile range (IQR)218.83993

Descriptive statistics

Standard deviation182.19669
Coefficient of variation (CV)0.43582443
Kurtosis1.2188998
Mean418.05067
Median Absolute Deviation (MAD)113.5278
Skewness0.96453998
Sum84028.184
Variance33195.636
MonotonicityNot monotonic
2024-05-22T04:30:54.315567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.4845403 1
 
0.5%
489.8515024 1
 
0.5%
621.1220299 1
 
0.5%
727.0036233 1
 
0.5%
1100.956788 1
 
0.5%
701.3716008 1
 
0.5%
568.441349 1
 
0.5%
418.0599161 1
 
0.5%
392.6496939 1
 
0.5%
550.7650278 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
125.8641867 1
0.5%
140.1693354 1
0.5%
145.9230739 1
0.5%
151.4749639 1
0.5%
153.9302527 1
0.5%
154.3671146 1
0.5%
154.4845403 1
0.5%
161.8533617 1
0.5%
166.985944 1
0.5%
169.4738719 1
0.5%
ValueCountFrequency (%)
1100.956788 1
0.5%
1012.217505 1
0.5%
979.6005366 1
0.5%
940.0079704 1
0.5%
932.865081 1
0.5%
871.9898517 1
0.5%
820.1253582 1
0.5%
753.1712566 1
0.5%
746.2882724 1
0.5%
745.0585067 1
0.5%

samp_en_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17244298
Minimum0.0134314
Maximum0.50285898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:54.472604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0134314
5-th percentile0.03161697
Q10.071074603
median0.13010339
Q30.25896954
95-th percentile0.41168147
Maximum0.50285898
Range0.48942758
Interquartile range (IQR)0.18789493

Descriptive statistics

Standard deviation0.12392442
Coefficient of variation (CV)0.71863998
Kurtosis-0.604684
Mean0.17244298
Median Absolute Deviation (MAD)0.081098046
Skewness0.71180147
Sum34.661039
Variance0.015357262
MonotonicityNot monotonic
2024-05-22T04:30:54.621636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.013796992 1
 
0.5%
0.470661893 1
 
0.5%
0.3648993023 1
 
0.5%
0.3282502496 1
 
0.5%
0.3165899218 1
 
0.5%
0.3501876119 1
 
0.5%
0.2699632135 1
 
0.5%
0.4116814745 1
 
0.5%
0.163556042 1
 
0.5%
0.3233820192 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.0134314001 1
0.5%
0.013796992 1
0.5%
0.0173978738 1
0.5%
0.0176413819 1
0.5%
0.0205475258 1
0.5%
0.0247647631 1
0.5%
0.0248711913 1
0.5%
0.0258672906 1
0.5%
0.0309048 1
0.5%
0.0312982461 1
0.5%
ValueCountFrequency (%)
0.5028589838 1
0.5%
0.4834190299 1
0.5%
0.470661893 1
0.5%
0.4415747695 1
0.5%
0.4371070826 1
0.5%
0.4351661662 1
0.5%
0.4257845323 1
0.5%
0.4222722264 1
0.5%
0.4158640167 1
0.5%
0.4150147816 1
0.5%

samp_en_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27735281
Minimum0.071889726
Maximum0.70512911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:54.765693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.071889726
5-th percentile0.089107921
Q10.16041173
median0.25374468
Q30.36955441
95-th percentile0.59072234
Maximum0.70512911
Range0.63323939
Interquartile range (IQR)0.20914268

Descriptive statistics

Standard deviation0.14701521
Coefficient of variation (CV)0.5300657
Kurtosis0.11869231
Mean0.27735281
Median Absolute Deviation (MAD)0.098765999
Skewness0.83150753
Sum55.747914
Variance0.021613472
MonotonicityNot monotonic
2024-05-22T04:30:54.935733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1030590937 1
 
0.5%
0.3695544072 1
 
0.5%
0.608390596 1
 
0.5%
0.505326861 1
 
0.5%
0.4771963569 1
 
0.5%
0.6335139089 1
 
0.5%
0.4897266732 1
 
0.5%
0.4190883086 1
 
0.5%
0.422593589 1
 
0.5%
0.3472300034 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.0718897256 1
0.5%
0.0725219629 1
0.5%
0.0762431572 1
0.5%
0.083465135 1
0.5%
0.0838249397 1
0.5%
0.0849476893 1
0.5%
0.0849994428 1
0.5%
0.0851895345 1
0.5%
0.0862282308 1
0.5%
0.0883682101 1
0.5%
ValueCountFrequency (%)
0.7051291116 1
0.5%
0.666411737 1
0.5%
0.66100706 1
0.5%
0.6335139089 1
0.5%
0.6310387943 1
0.5%
0.6251620562 1
0.5%
0.622644115 1
0.5%
0.6171764323 1
0.5%
0.608390596 1
0.5%
0.6054771058 1
0.5%

f80_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.10775
Minimum98.658589
Maximum2265.5144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:55.101806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum98.658589
5-th percentile190.46012
Q1361.11883
median603.95829
Q3936.0583
95-th percentile1511.1197
Maximum2265.5144
Range2166.8558
Interquartile range (IQR)574.93947

Descriptive statistics

Standard deviation411.87635
Coefficient of variation (CV)0.60030856
Kurtosis1.6422728
Mean686.10775
Median Absolute Deviation (MAD)276.36321
Skewness1.1314186
Sum137907.66
Variance169642.13
MonotonicityNot monotonic
2024-05-22T04:30:55.315852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.64966 1
 
0.5%
1240.133506 1
 
0.5%
1058.999871 1
 
0.5%
1540.270299 1
 
0.5%
2253.309346 1
 
0.5%
1083.585994 1
 
0.5%
1083.88716 1
 
0.5%
707.7382861 1
 
0.5%
456.4503005 1
 
0.5%
1225.588598 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
98.65858885 1
0.5%
125.0003259 1
0.5%
132.64966 1
0.5%
166.0776521 1
0.5%
166.1947372 1
0.5%
166.3213434 1
0.5%
181.975262 1
0.5%
183.655881 1
0.5%
184.0924451 1
0.5%
189.4398314 1
0.5%
ValueCountFrequency (%)
2265.51438 1
0.5%
2253.309346 1
0.5%
2135.675208 1
0.5%
1712.937409 1
0.5%
1639.26448 1
0.5%
1614.594799 1
0.5%
1544.639175 1
0.5%
1544.029487 1
0.5%
1540.270299 1
0.5%
1511.774397 1
0.5%

f80_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean992.04357
Minimum370.84685
Maximum3216.9799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:55.469885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum370.84685
5-th percentile430.51698
Q1668.06152
median887.44404
Q31143.1532
95-th percentile2003.6314
Maximum3216.9799
Range2846.133
Interquartile range (IQR)475.09172

Descriptive statistics

Standard deviation497.68326
Coefficient of variation (CV)0.5016748
Kurtosis3.1497202
Mean992.04357
Median Absolute Deviation (MAD)235.36767
Skewness1.6330039
Sum199400.76
Variance247688.63
MonotonicityNot monotonic
2024-05-22T04:30:55.635943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448.7874142 1
 
0.5%
1118.165532 1
 
0.5%
2003.631374 1
 
0.5%
1780.975907 1
 
0.5%
2636.217542 1
 
0.5%
1996.38941 1
 
0.5%
1484.197101 1
 
0.5%
1077.797924 1
 
0.5%
1153.867934 1
 
0.5%
1082.310159 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
370.8468533 1
0.5%
372.6425931 1
0.5%
383.2227838 1
0.5%
388.6607837 1
0.5%
404.0442544 1
0.5%
406.2054824 1
0.5%
411.1419924 1
0.5%
411.4052238 1
0.5%
423.2777782 1
0.5%
427.3969426 1
0.5%
ValueCountFrequency (%)
3216.979883 1
0.5%
2648.329584 1
0.5%
2636.217542 1
0.5%
2577.197098 1
0.5%
2473.022989 1
0.5%
2450.988083 1
0.5%
2260.574595 1
0.5%
2222.648337 1
0.5%
2083.588336 1
0.5%
2079.179135 1
0.5%

mf_lf_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.073776
Minimum7.6144221
Maximum175.67664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:55.839989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.6144221
5-th percentile11.933359
Q123.178266
median40.721786
Q360.808386
95-th percentile91.779356
Maximum175.67664
Range168.06222
Interquartile range (IQR)37.630121

Descriptive statistics

Standard deviation28.485496
Coefficient of variation (CV)0.63197492
Kurtosis3.7438825
Mean45.073776
Median Absolute Deviation (MAD)18.58805
Skewness1.5324775
Sum9059.8289
Variance811.42346
MonotonicityNot monotonic
2024-05-22T04:30:56.021029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.614422075 1
 
0.5%
48.96382782 1
 
0.5%
42.42954261 1
 
0.5%
118.7344507 1
 
0.5%
175.6766434 1
 
0.5%
77.18701493 1
 
0.5%
76.58206833 1
 
0.5%
15.17998114 1
 
0.5%
19.53826759 1
 
0.5%
91.77935627 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
7.614422075 1
0.5%
7.93257393 1
0.5%
8.623364292 1
0.5%
9.218863295 1
0.5%
9.87616023 1
0.5%
10.91292898 1
0.5%
11.34696334 1
0.5%
11.58891159 1
0.5%
11.61431705 1
0.5%
11.92935494 1
0.5%
ValueCountFrequency (%)
175.6766434 1
0.5%
166.0783525 1
0.5%
146.0178559 1
0.5%
129.5774906 1
0.5%
129.3596394 1
0.5%
118.7344507 1
0.5%
117.5658507 1
0.5%
103.058141 1
0.5%
96.10983637 1
0.5%
95.02405055 1
0.5%

mf_lf_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.342078
Minimum21.443016
Maximum139.95256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:56.188067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21.443016
5-th percentile30.860042
Q139.455266
median53.21461
Q369.45322
95-th percentile96.456619
Maximum139.95256
Range118.50954
Interquartile range (IQR)29.997954

Descriptive statistics

Standard deviation21.907487
Coefficient of variation (CV)0.38882994
Kurtosis0.68618469
Mean56.342078
Median Absolute Deviation (MAD)14.339587
Skewness0.90907048
Sum11324.758
Variance479.93798
MonotonicityNot monotonic
2024-05-22T04:30:56.346104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.26303929 1
 
0.5%
57.52178018 1
 
0.5%
90.44012153 1
 
0.5%
83.03008146 1
 
0.5%
100.548046 1
 
0.5%
72.14086371 1
 
0.5%
77.07510775 1
 
0.5%
56.56303411 1
 
0.5%
63.42410099 1
 
0.5%
50.7151846 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
21.44301649 1
0.5%
21.84574564 1
0.5%
22.28147854 1
0.5%
22.71439948 1
0.5%
22.88330344 1
0.5%
23.02964085 1
0.5%
24.97049361 1
0.5%
27.5867845 1
0.5%
27.6540338 1
0.5%
29.42955016 1
0.5%
ValueCountFrequency (%)
139.9525575 1
0.5%
119.2852093 1
0.5%
116.0153188 1
0.5%
114.8685069 1
0.5%
110.382772 1
0.5%
104.4706883 1
0.5%
104.3503047 1
0.5%
104.2814477 1
0.5%
100.548046 1
0.5%
99.62205304 1
0.5%

mf_mf_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.463452
Minimum1.2381509
Maximum36.936324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:56.509164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.2381509
5-th percentile2.9509801
Q15.149696
median9.6391325
Q316.235964
95-th percentile26.864156
Maximum36.936324
Range35.698173
Interquartile range (IQR)11.086268

Descriptive statistics

Standard deviation7.863357
Coefficient of variation (CV)0.68595016
Kurtosis0.57675739
Mean11.463452
Median Absolute Deviation (MAD)5.0806921
Skewness1.0593718
Sum2304.1539
Variance61.832384
MonotonicityNot monotonic
2024-05-22T04:30:56.672198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.322267826 1
 
0.5%
31.47097986 1
 
0.5%
26.60137966 1
 
0.5%
20.58780672 1
 
0.5%
31.00595297 1
 
0.5%
16.26897548 1
 
0.5%
16.74201146 1
 
0.5%
22.18038689 1
 
0.5%
11.17467201 1
 
0.5%
18.41751943 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
1.238150934 1
0.5%
2.097441263 1
0.5%
2.322267826 1
0.5%
2.322984862 1
0.5%
2.452341973 1
0.5%
2.461067945 1
0.5%
2.58866797 1
0.5%
2.646297908 1
0.5%
2.66611638 1
0.5%
2.765346675 1
0.5%
ValueCountFrequency (%)
36.93632375 1
0.5%
35.96059068 1
0.5%
34.363621 1
0.5%
32.89732363 1
0.5%
31.47097986 1
0.5%
31.00595297 1
0.5%
30.56587171 1
0.5%
28.56622673 1
0.5%
28.54434944 1
0.5%
27.68421602 1
0.5%

mf_mf_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.822807
Minimum4.632193
Maximum72.852129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:56.838283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.632193
5-th percentile6.6435994
Q110.478873
median15.931432
Q322.016921
95-th percentile46.780313
Maximum72.852129
Range68.219936
Interquartile range (IQR)11.538048

Descriptive statistics

Standard deviation12.804135
Coefficient of variation (CV)0.68024576
Kurtosis4.3070567
Mean18.822807
Median Absolute Deviation (MAD)6.0537075
Skewness1.9958334
Sum3783.3842
Variance163.94586
MonotonicityNot monotonic
2024-05-22T04:30:56.994450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.643599388 1
 
0.5%
23.93802131 1
 
0.5%
43.47470039 1
 
0.5%
36.8662628 1
 
0.5%
64.26423789 1
 
0.5%
46.78031321 1
 
0.5%
28.10853436 1
 
0.5%
22.0169206 1
 
0.5%
23.17738938 1
 
0.5%
22.16681711 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
4.632193048 1
0.5%
5.005743343 1
0.5%
5.423701522 1
0.5%
5.58786641 1
0.5%
5.626381375 1
0.5%
5.694471378 1
0.5%
5.962746789 1
0.5%
6.253076598 1
0.5%
6.324406157 1
0.5%
6.552386904 1
0.5%
ValueCountFrequency (%)
72.85212917 1
0.5%
66.02949957 1
0.5%
65.85483778 1
0.5%
64.26423789 1
0.5%
59.3816492 1
0.5%
57.22066757 1
0.5%
57.19427642 1
0.5%
57.12197481 1
0.5%
55.96491817 1
0.5%
46.80089101 1
0.5%

mf_hf_x
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5748339
Minimum0.17132422
Maximum7.2847193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:57.147484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.17132422
5-th percentile0.31155108
Q10.67951993
median1.2656056
Q32.2709891
95-th percentile3.608365
Maximum7.2847193
Range7.1133951
Interquartile range (IQR)1.5914692

Descriptive statistics

Standard deviation1.1389721
Coefficient of variation (CV)0.72323316
Kurtosis3.3531728
Mean1.5748339
Median Absolute Deviation (MAD)0.682154
Skewness1.479017
Sum316.54162
Variance1.2972575
MonotonicityNot monotonic
2024-05-22T04:30:57.334525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.499995488 1
 
0.5%
2.909980217 1
 
0.5%
2.535325562 1
 
0.5%
3.00897912 1
 
0.5%
3.24229149 1
 
0.5%
2.363576974 1
 
0.5%
2.169448083 1
 
0.5%
1.686535987 1
 
0.5%
0.9985009818 1
 
0.5%
1.541665043 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.1713242186 1
0.5%
0.1773381646 1
0.5%
0.2023647979 1
0.5%
0.2177225409 1
0.5%
0.2317100426 1
0.5%
0.2511905004 1
0.5%
0.2655010763 1
0.5%
0.2663607454 1
0.5%
0.2897514264 1
0.5%
0.3103054013 1
0.5%
ValueCountFrequency (%)
7.284719289 1
0.5%
5.873398682 1
0.5%
4.843174172 1
0.5%
4.727016224 1
0.5%
4.413559436 1
0.5%
4.376797448 1
0.5%
3.957925653 1
0.5%
3.936606647 1
0.5%
3.853071948 1
0.5%
3.803938943 1
0.5%

mf_hf_y
Real number (ℝ)

UNIQUE 

Distinct201
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.798737
Minimum0.36418126
Maximum13.513164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-22T04:30:57.502646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.36418126
5-th percentile0.81296813
Q11.4301043
median2.1728586
Q33.3448426
95-th percentile7.3856755
Maximum13.513164
Range13.148983
Interquartile range (IQR)1.9147383

Descriptive statistics

Standard deviation2.206219
Coefficient of variation (CV)0.78829091
Kurtosis5.0389692
Mean2.798737
Median Absolute Deviation (MAD)0.88296002
Skewness2.1271787
Sum562.54615
Variance4.8674021
MonotonicityNot monotonic
2024-05-22T04:30:57.676137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7261473306 1
 
0.5%
2.608711862 1
 
0.5%
7.397424743 1
 
0.5%
7.248279625 1
 
0.5%
9.046608287 1
 
0.5%
9.266718234 1
 
0.5%
6.030981694 1
 
0.5%
3.027736139 1
 
0.5%
2.819305646 1
 
0.5%
4.518283496 1
 
0.5%
Other values (191) 191
95.0%
ValueCountFrequency (%)
0.3641812612 1
0.5%
0.6083680241 1
0.5%
0.6637964436 1
0.5%
0.701347127 1
0.5%
0.7020305964 1
0.5%
0.7042350312 1
0.5%
0.7190041287 1
0.5%
0.7261473306 1
0.5%
0.7440249463 1
0.5%
0.7892973859 1
0.5%
ValueCountFrequency (%)
13.51316408 1
0.5%
10.90338507 1
0.5%
10.84096912 1
0.5%
10.56552251 1
0.5%
9.946866523 1
0.5%
9.266718234 1
0.5%
9.046608287 1
0.5%
8.325813614 1
0.5%
7.912330385 1
0.5%
7.397424743 1
0.5%

Interactions

2024-05-22T04:30:50.111600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:37.084343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:38.098307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:39.140634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:40.149575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:41.161860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:42.454790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:43.470860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-22T04:30:44.498883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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Missing values

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A simple visualization of nullity by column.
2024-05-22T04:30:51.515264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

classfile_pathexamcop_xcop_yflagacc_xacc_yrms_acc_xrms_acc_ypathsamp_en_xsamp_en_yf80_xf80_ymf_lf_xmf_lf_ymf_mf_xmf_mf_ymf_hf_xmf_hf_y
232Neuropathic../data/raw/COP_Tests/DNG\participant_12\EBOA 1_SWARII.txtEBOA[0.1573173094, 0.16568541920000002, 0.17469752430000002, 0.1835473485, 0.183844836, 0.1721494547, 0.17801854790000002, 0.1778272098, 0.1874362131, 0.2055584419, 0.2213108385, 0.2427932069, 0.25659933420000003, 0.2703099211, 0.2757443519, 0.269668624, 0.2696106014, 0.2665461745, 0.2619406965, 0.2530857634, 0.2383634917, 0.22581733680000002, 0.2097007165, 0.1981402428, 0.1758915077, 0.1608928937, 0.1476680256, 0.1327854922, 0.1276036384, 0.1156060185, 0.10603645440000001, 0.0969200738, 0.0892727733, 0.0789867635, 0.0654385213, 0.058794487900000005, 0.0520711379, 0.0350032863, 0.0287130667, 0.0216388448, 0.014658085400000001, 0.0047444566, -0.0048906654, -0.010481618600000001, -0.0204194974, -0.0276494125, -0.0240705337, -0.0262342123, -0.027513009600000003, -0.0189568045, -0.0084570591, -6.03902e-05, 0.0043109105, 0.0145798021, 0.0246136486, 0.0376554434, 0.053733729, 0.0678469563, 0.0839725796, 0.0991072355, 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